MMR-and-RMR-workflow-Clutch49

Load libraries (packages)

library("respR") ## respirometry/slope analysis
library("tidyverse") ## data manipulation

Set working directory

setwd("[PATH TO DIRECTORY]")

System1 - Dell

Importing data from firesting for resting

preexperiment_date <- "27 May 2023 11 28AM/All"
postexperiment_date <- "27 May 2023 04 31PM/All"

##--- last fish run in trial ---##
experiment_date <- "27 May 2023 01 02PM/Oxygen"
experiment_date2 <- "27 May 2023 01 02PM/All"

firesting <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19)

Cycle_1 <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Cycle_last <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_21.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

System2 - Asus

Importing data from firesting for resting

preexperiment_date_asus <- "27 May 2023 12 44PM/All"
postexperiment_date_asus <- "27 May 2023 05 09PM/All"

##--- last fish run in trial ---##
experiment_date_asus <- "27 May 2023 01 59PM/Oxygen"
experiment_date2_asus <- "27 May 2023 01 59PM/All"

firesting_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19)

Cycle_1_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Cycle_last_asus <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_21.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Chamber volumes

chamber1_dell = 0.04650#+0.00022
chamber2_dell = 0.04593#+0.00022
chamber3_dell = 0.04977#+0.00022
chamber4_dell = 0.04860#+0.00022

chamber1_asus = 0.04565
chamber2_asus = 0.04573#+0.00385
chamber3_asus = 0.04551#+0.00322
chamber4_asus = 0.04791#+0.00277

Date_tested="2023-05-27"
Clutch = "112" 
Male = "CARL355" 
Female = "CARL354"
Population = "Arlginton reef"
Tank =221 
salinity =37 
Date_analysed = Sys.Date() 

Replicates

1

Enter specimen data

Replicate = 1 
mass = 0.0006263
chamber = "ch4" 
Swim = "good/good"
chamber_vol = chamber4_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "27 May 2023 12 32PM/Oxygen"
experiment_mmr_date2 <- "27 May 2023 12 32PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] -0.0009579832

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.002265395

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  8  9 16 17 18 19 21 22 23 24 25 26 27 28 31 32
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 3.11
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1] 70 71 75 76 79 80 82 83 84 85 86 87 88 89 90 91 92 93 94 95
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 3.11
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=15, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density   row endrow     time
## 1:  11    1     307.2112 -0.01847437 0.990      NA  5401   5656 11260.13
## 2:  16    1     299.4143 -0.01432965 0.980      NA  8101   8356 13960.13
## 3:  18    1     343.0714 -0.01621790 0.992      NA  9181   9436 15040.13
## 4:  19    1     403.3993 -0.01950402 0.953      NA  9721   9976 15580.13
## 5:  20    1     414.0002 -0.01952848 0.979      NA 10261  10516 16120.13
## 6:  21    1     397.2991 -0.01786620 0.973      NA 10801  11056 16660.13
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 11515.13 98.942 94.364 -0.01847437 -0.001614948   -0.01685942 -0.01685942
## 2: 14215.13 98.988 95.316 -0.01432965 -0.001933368   -0.01239629 -0.01239629
## 3: 15295.13 99.029 94.764 -0.01621790 -0.002060736   -0.01415717 -0.01415717
## 4: 15835.13 98.980 93.929 -0.01950402 -0.002124421   -0.01737959 -0.01737959
## 5: 16375.13 99.154 94.749 -0.01952848 -0.002188105   -0.01734037 -0.01734037
## 6: 16915.13 99.171 94.231 -0.01786620 -0.002251789   -0.01561442 -0.01561442
##    oxy.unit time.unit volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0486 0.0006263   NA 37 27 1.013253 -0.1904174
## 2:     %Air       sec 0.0486 0.0006263   NA 37 27 1.013253 -0.1400089
## 3:     %Air       sec 0.0486 0.0006263   NA 37 27 1.013253 -0.1598970
## 4:     %Air       sec 0.0486 0.0006263   NA 37 27 1.013253 -0.1962924
## 5:     %Air       sec 0.0486 0.0006263   NA 37 27 1.013253 -0.1958495
## 6:     %Air       sec 0.0486 0.0006263   NA 37 27 1.013253 -0.1763558
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -304.0355          NA  mgO2/hr/kg   -304.0355
## 2:   -223.5492          NA  mgO2/hr/kg   -223.5492
## 3:   -255.3042          NA  mgO2/hr/kg   -255.3042
## 4:   -313.4160          NA  mgO2/hr/kg   -313.4160
## 5:   -312.7087          NA  mgO2/hr/kg   -312.7087
## 6:   -281.5835          NA  mgO2/hr/kg   -281.5835
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
112	1	CARL355	CARL354	Arlginton reef	221	0.0006263	ch4	Dell	0.0486	2023-05-27	2024-08-16	good/good	37	27	293.4096	0.1837624	0.9774

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  8  9 16 17 18 19 21 22 23 24 25 26 27 28 31 32
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 1.64
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  9 10 11 12 13 14 15 17 18 19 20 21 22 27 28 30 33 34 38
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 1.11
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##        rep  rank intercept_b0     slope_b1       rsq density   row endrow
##      <num> <int>        <num>        <num>     <num>  <lgcl> <int>  <int>
##   1:    NA     1     355.0635 -0.061502267 0.9755254      NA   169    230
##   2:    NA     2     354.5026 -0.061368667 0.9750083      NA   170    231
##   3:    NA     3     353.8294 -0.061214956 0.9741925      NA   167    228
##   4:    NA     4     352.9775 -0.061008582 0.9752590      NA   171    232
##   5:    NA     5     352.7392 -0.060957348 0.9748715      NA   168    229
##  ---                                                                     
## 236:    NA   236     121.1348 -0.005998345 0.9225698      NA    82    143
## 237:    NA   237     121.1329 -0.005997780 0.9225360      NA    83    144
## 238:    NA   238     120.9337 -0.005950000 0.9224393      NA    84    145
## 239:    NA   239     120.8329 -0.005926005 0.9231307      NA    86    147
## 240:    NA   240     120.6461 -0.005881002 0.9229719      NA    85    146
##         time endtime    oxy endoxy         rate
##        <num>   <num>  <num>  <num>        <num>
##   1: 4213.59 4273.59 95.670 92.431 -0.061502267
##   2: 4214.56 4274.56 95.671 92.397 -0.061368667
##   3: 4211.56 4271.56 95.667 92.456 -0.061214956
##   4: 4215.55 4275.55 95.717 92.345 -0.061008582
##   5: 4212.55 4272.55 95.661 92.448 -0.060957348
##  ---                                           
## 236: 4126.59 4186.59 96.396 96.045 -0.005998345
## 237: 4127.56 4187.56 96.369 96.028 -0.005997780
## 238: 4128.55 4188.55 96.402 96.012 -0.005950000
## 239: 4130.55 4190.55 96.357 95.974 -0.005926005
## 240: 4129.55 4189.55 96.406 96.008 -0.005881002
## 
## Regressions : 240 | Results : 240 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 240 adjusted rate(s):
## Rate          : -0.06150227
## Adjustment    : -0.0009579832
## Adjusted Rate : -0.06054428 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 106 rate(s) removed, 134 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 133 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     355.0635 -0.06150227 0.9755254      NA 169    230 4213.59
##    endtime   oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1: 4273.59 95.67 92.431 -0.06150227 -0.0009579832   -0.06054428 -0.06054428
##    oxy.unit time.unit volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0486 0.0006263   NA 37 27 1.013253 -0.6838126
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1091.829          NA  mgO2/hr/kg   -1091.829
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
112	1	CARL355	CARL354	Arlginton reef	221	0.0006263	ch4	Dell	0.0486	2023-05-27	2024-08-16	good/good	37	27	293.4096	0.1837624	0.9774	1091.829	0.6838126	0.9755254	798.4195	0.5000501

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 365 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

2

Enter specimen data

Replicate = 2 
mass = 0.0005381 
chamber = "ch3" 
Swim = "good/good"
chamber_vol = chamber3_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "27 May 2023 12 42PM/Oxygen"
experiment_mmr_date2 <- "27 May 2023 12 42PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] -0.001885896

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post

## [1] -0.003445393

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  8  9 16 17 18 19 21 22 23 24 25 26 27 28 31 32
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 3.11
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1] 70 71 75 76 79 80 82 83 84 85 86 87 88 89 90 91 92 93 94 95
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 3.11
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=15, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density   row endrow     time
## 1:   8    1     230.0593 -0.01358043 0.986      NA  3781   4036  9640.13
## 2:  12    1     268.1471 -0.01428831 0.973      NA  5941   6196 11800.13
## 3:  16    1     302.0362 -0.01452096 0.989      NA  8101   8356 13960.13
## 4:  17    1     320.0263 -0.01522889 0.994      NA  8641   8896 14500.13
## 5:  19    1     335.3709 -0.01515080 0.994      NA  9721   9976 15580.13
## 6:  20    1     332.8504 -0.01448012 0.993      NA 10261  10516 16120.13
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1:  9895.14 98.848 95.498 -0.01358043 -0.002441642   -0.01113879 -0.01113879
## 2: 12055.13 99.212 95.577 -0.01428831 -0.002745494   -0.01154282 -0.01154282
## 3: 14215.13 98.987 95.379 -0.01452096 -0.003049347   -0.01147161 -0.01147161
## 4: 14755.13 98.998 95.211 -0.01522889 -0.003125310   -0.01210358 -0.01210358
## 5: 15835.13 99.081 95.382 -0.01515080 -0.003277237   -0.01187357 -0.01187357
## 6: 16375.13 99.290 95.586 -0.01448012 -0.003353200   -0.01112692 -0.01112692
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04977 0.0005381   NA 37 27 1.013253 -0.1288348
## 2:     %Air       sec 0.04977 0.0005381   NA 37 27 1.013253 -0.1335080
## 3:     %Air       sec 0.04977 0.0005381   NA 37 27 1.013253 -0.1326844
## 4:     %Air       sec 0.04977 0.0005381   NA 37 27 1.013253 -0.1399939
## 5:     %Air       sec 0.04977 0.0005381   NA 37 27 1.013253 -0.1373335
## 6:     %Air       sec 0.04977 0.0005381   NA 37 27 1.013253 -0.1286976
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -239.4254          NA  mgO2/hr/kg   -239.4254
## 2:   -248.1100          NA  mgO2/hr/kg   -248.1100
## 3:   -246.5794          NA  mgO2/hr/kg   -246.5794
## 4:   -260.1633          NA  mgO2/hr/kg   -260.1633
## 5:   -255.2193          NA  mgO2/hr/kg   -255.2193
## 6:   -239.1704          NA  mgO2/hr/kg   -239.1704
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
112	2	CARL355	CARL354	Arlginton reef	221	0.0005381	ch3	Dell	0.04977	2023-05-27	2024-08-16	good/good	37	27	249.8995	0.1344709	0.9872

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  8  9 16 17 18 19 21 22 23 24 25 26 27 28 31 32
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 1.64
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row,  
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1] 42 43 44 47 48 49 52 53 54 55 56 57 58 59 60 63 64 65 67 68
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 1.09
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##        rep  rank intercept_b0    slope_b1       rsq density   row endrow
##      <num> <int>        <num>       <num>     <num>  <lgcl> <int>  <int>
##   1:    NA     1     299.1767 -0.04263369 0.9654999      NA    89    149
##   2:    NA     2     298.9706 -0.04259186 0.9651468      NA    88    148
##   3:    NA     3     298.7515 -0.04254315 0.9647579      NA    90    150
##   4:    NA     4     298.6241 -0.04252020 0.9646657      NA    87    147
##   5:    NA     5     298.3325 -0.04245394 0.9640196      NA    91    151
##  ---                                                                    
## 236:    NA   236     222.0159 -0.02670058 0.9861303      NA   236    296
## 237:    NA   237     221.7246 -0.02664326 0.9866613      NA   240    300
## 238:    NA   238     221.6277 -0.02662316 0.9867486      NA   239    299
## 239:    NA   239     221.4622 -0.02658852 0.9868931      NA   237    297
## 240:    NA   240     221.3940 -0.02657520 0.9869799      NA   238    298
##         time endtime    oxy endoxy        rate
##        <num>   <num>  <num>  <num>       <num>
##   1: 4737.55 4797.55 97.031 94.828 -0.04263369
##   2: 4736.55 4796.55 97.019 94.805 -0.04259186
##   3: 4738.55 4798.55 96.991 94.826 -0.04254315
##   4: 4735.55 4795.55 97.097 94.780 -0.04252020
##   5: 4739.55 4799.55 97.036 94.793 -0.04245394
##  ---                                          
## 236: 4884.55 4944.55 91.714 89.959 -0.02670058
## 237: 4888.55 4948.55 91.562 89.815 -0.02664326
## 238: 4887.55 4947.55 91.558 89.831 -0.02662316
## 239: 4885.55 4945.55 91.648 89.915 -0.02658852
## 240: 4886.55 4946.55 91.582 89.861 -0.02657520
## 
## Regressions : 240 | Results : 240 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 240 adjusted rate(s):
## Rate          : -0.04263369
## Adjustment    : -0.001885896
## Adjusted Rate : -0.04074779 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 0 rate(s) removed, 240 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 239 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     299.1767 -0.04263369 0.9654999      NA  89    149 4737.55
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 4797.55 97.031 94.828 -0.04263369 -0.001885896   -0.04074779 -0.04074779
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04977 0.0005381   NA 37 27 1.013253 -0.4713021
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -875.8634          NA  mgO2/hr/kg   -875.8634
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
112	2	CARL355	CARL354	Arlginton reef	221	0.0005381	ch3	Dell	0.04977	2023-05-27	2024-08-16	good/good	37	27	249.8995	0.1344709	0.9872	875.8634	0.4713021	0.9654999	625.9639	0.3368312

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 366 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

3

Enter specimen data

Replicate = 3 
mass = 0.0005153 
chamber = "ch2" 
Swim = "good/good"
chamber_vol = chamber2_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "27 May 2023 12 52PM/Oxygen"
experiment_mmr_date2 <- "27 May 2023 12 52PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] -0.001526609

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post

## [1] -0.003501353

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  8  9 16 17 18 19 21 22 23 24 25 26 27 28 31 32
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 3.11
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1] 70 71 75 76 79 80 82 83 84 85 86 87 88 89 90 91 92 93 94 95
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 3.11
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 4 rate(s) removed, 17 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 11 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density   row endrow     time
## 1:   3    1     213.2555 -0.01627153 0.963      NA  1114   1367  6971.02
## 2:   9    1     266.9509 -0.01641365 0.986      NA  4351   4606 10210.13
## 3:  13    1     334.7950 -0.01905856 0.981      NA  6511   6766 12370.13
## 4:  16    1     334.6238 -0.01680877 0.976      NA  8131   8386 13990.13
## 5:  17    1     367.1159 -0.01843354 0.995      NA  8671   8926 14530.13
## 6:  20    1     402.3605 -0.01876519 0.982      NA 10291  10546 16150.13
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1:  7226.13 99.473 95.377 -0.01627153 -0.001754895   -0.01451663 -0.01451663
## 2: 10465.13 99.084 95.271 -0.01641365 -0.002331867   -0.01408178 -0.01408178
## 3: 12625.13 98.912 94.579 -0.01905856 -0.002716626   -0.01634194 -0.01634194
## 4: 14245.13 99.272 94.950 -0.01680877 -0.003005196   -0.01380358 -0.01380358
## 5: 14785.13 99.271 94.438 -0.01843354 -0.003101386   -0.01533215 -0.01533215
## 6: 16405.13 99.110 94.885 -0.01876519 -0.003389956   -0.01537523 -0.01537523
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04593 0.0005153   NA 37 27 1.013253 -0.1549494
## 2:     %Air       sec 0.04593 0.0005153   NA 37 27 1.013253 -0.1503078
## 3:     %Air       sec 0.04593 0.0005153   NA 37 27 1.013253 -0.1744326
## 4:     %Air       sec 0.04593 0.0005153   NA 37 27 1.013253 -0.1473383
## 5:     %Air       sec 0.04593 0.0005153   NA 37 27 1.013253 -0.1636542
## 6:     %Air       sec 0.04593 0.0005153   NA 37 27 1.013253 -0.1641140
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -300.6975          NA  mgO2/hr/kg   -300.6975
## 2:   -291.6900          NA  mgO2/hr/kg   -291.6900
## 3:   -338.5069          NA  mgO2/hr/kg   -338.5069
## 4:   -285.9273          NA  mgO2/hr/kg   -285.9273
## 5:   -317.5902          NA  mgO2/hr/kg   -317.5902
## 6:   -318.4825          NA  mgO2/hr/kg   -318.4825
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple")  

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
112	3	CARL355	CARL354	Arlginton reef	221	0.0005153	ch2	Dell	0.04593	2023-05-27	2024-08-16	good/good	37	27	313.3934	0.1614916	0.9814

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  8  9 16 17 18 19 21 22 23 24 25 26 27 28 31 32
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 1.64
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1] 31 32 33 38 39 43 44 45 46 47 48 49 50 52 53 54 55 56 57 58
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.96 1.07
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##        rep  rank intercept_b0    slope_b1       rsq density   row endrow
##      <num> <int>        <num>       <num>     <num>  <lgcl> <int>  <int>
##   1:    NA     1     732.6578 -0.11627599 0.9735278      NA   188    248
##   2:    NA     2     732.2203 -0.11619347 0.9732953      NA   189    249
##   3:    NA     3     731.8381 -0.11613016 0.9731123      NA   187    247
##   4:    NA     4     731.1793 -0.11600146 0.9727816      NA   190    250
##   5:    NA     5     729.4330 -0.11569640 0.9717829      NA   186    246
##  ---                                                                    
## 237:    NA   237     168.3512 -0.01321301 0.9416956      NA    77    137
## 238:    NA   238     168.0124 -0.01314818 0.9384164      NA    73    133
## 239:    NA   239     167.8344 -0.01311597 0.9396091      NA    74    134
## 240:    NA   240     167.6051 -0.01307446 0.9419688      NA    76    136
## 241:    NA   241     167.3565 -0.01302803 0.9423974      NA    75    135
##         time endtime    oxy endoxy        rate
##        <num>   <num>  <num>  <num>       <num>
##   1: 5473.55 5533.55 95.737 89.712 -0.11627599
##   2: 5474.55 5534.55 95.686 89.669 -0.11619347
##   3: 5472.55 5532.55 95.751 89.770 -0.11613016
##   4: 5475.55 5535.55 95.636 89.583 -0.11600146
##   5: 5471.55 5531.55 95.711 89.862 -0.11569640
##  ---                                          
## 237: 5362.55 5422.55 97.411 96.632 -0.01321301
## 238: 5358.55 5418.55 97.701 96.612 -0.01314818
## 239: 5359.55 5419.55 97.681 96.627 -0.01311597
## 240: 5361.55 5421.55 97.495 96.657 -0.01307446
## 241: 5360.55 5420.55 97.556 96.647 -0.01302803
## 
## Regressions : 241 | Results : 241 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 241 adjusted rate(s):
## Rate          : -0.116276
## Adjustment    : -0.001526609
## Adjusted Rate : -0.1147494 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 85 rate(s) removed, 156 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 155 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0  slope_b1       rsq density row endrow    time endtime
## 1:  NA    1     732.6578 -0.116276 0.9735278      NA 188    248 5473.55 5533.55
##       oxy endoxy      rate   adjustment rate.adjusted rate.input oxy.unit
## 1: 95.737 89.712 -0.116276 -0.001526609    -0.1147494 -0.1147494     %Air
##    time.unit  volume      mass area  S  t        P  rate.abs rate.m.spec
## 1:       sec 0.04593 0.0005153   NA 37 27 1.013253 -1.224826   -2376.919
##    rate.a.spec output.unit rate.output
## 1:          NA  mgO2/hr/kg   -2376.919
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
112	3	CARL355	CARL354	Arlginton reef	221	0.0005153	ch2	Dell	0.04593	2023-05-27	2024-08-16	good/good	37	27	313.3934	0.1614916	0.9814	2376.919	1.224826	0.9735278	2063.525	1.063335

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 367 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

4

Enter specimen data

Replicate = 4 
mass = 0.0005249
chamber = "ch1" 
Swim = "good/good"
chamber_vol = chamber1_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "27 May 2023 01 02PM/Oxygen"
experiment_mmr_date2 <- "27 May 2023 01 02PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] -0.001307744

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean, bg_post3$rate.bg.mean)  
bg_post 

## [1] -0.002225272

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  8  9 16 17 18 19 21 22 23 24 25 26 27 28 31 32
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 3.11
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1] 70 71 75 76 79 80 82 83 84 85 86 87 88 89 90 91 92 93 94 95
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 3.11
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=15, 
                              measure=255, 
                              by="time", 
                              plot=TRUE)  

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density   row endrow     time
## 1:  13    1     376.4524 -0.02248276 0.993      NA  6481   6736 12340.13
## 2:  16    1     351.4288 -0.01807506 0.993      NA  8101   8356 13960.13
## 3:  17    1     414.9115 -0.02176330 0.993      NA  8641   8896 14500.13
## 4:  19    1     424.9152 -0.02090945 0.994      NA  9721   9976 15580.13
## 5:  20    1     427.9766 -0.02039620 0.979      NA 10261  10516 16120.13
## 6:  21    1     485.1253 -0.02312841 0.993      NA 10801  11056 16660.13
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 12595.13 98.859 93.168 -0.02248276 -0.001858180   -0.02062458 -0.02062458
## 2: 14215.13 98.994 94.622 -0.01807506 -0.001992259   -0.01608280 -0.01608280
## 3: 14755.13 98.916 93.755 -0.02176330 -0.002036952   -0.01972634 -0.01972634
## 4: 15835.13 98.981 93.750 -0.02090945 -0.002126338   -0.01878311 -0.01878311
## 5: 16375.13 98.822 93.667 -0.02039620 -0.002171031   -0.01822517 -0.01822517
## 6: 16915.13 99.459 93.698 -0.02312841 -0.002215723   -0.02091269 -0.02091269
##    oxy.unit time.unit volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0465 0.0005249   NA 37 27 1.013253 -0.2228773
## 2:     %Air       sec 0.0465 0.0005249   NA 37 27 1.013253 -0.1737970
## 3:     %Air       sec 0.0465 0.0005249   NA 37 27 1.013253 -0.2131706
## 4:     %Air       sec 0.0465 0.0005249   NA 37 27 1.013253 -0.2029776
## 5:     %Air       sec 0.0465 0.0005249   NA 37 27 1.013253 -0.1969482
## 6:     %Air       sec 0.0465 0.0005249   NA 37 27 1.013253 -0.2259907
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -424.6090          NA  mgO2/hr/kg   -424.6090
## 2:   -331.1049          NA  mgO2/hr/kg   -331.1049
## 3:   -406.1165          NA  mgO2/hr/kg   -406.1165
## 4:   -386.6977          NA  mgO2/hr/kg   -386.6977
## 5:   -375.2110          NA  mgO2/hr/kg   -375.2110
## 6:   -430.5404          NA  mgO2/hr/kg   -430.5404
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple")  

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
112	4	CARL355	CARL354	Arlginton reef	221	0.0005249	ch1	Dell	0.0465	2023-05-27	2024-08-16	good/good	37	27	404.6349	0.2123929	0.9904

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  8  9 16 17 18 19 21 22 23 24 25 26 27 28 31 32
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 3.11
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1] 70 71 75 76 79 80 82 83 84 85 86 87 88 89 90 91 92 93 94 95
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 1.07
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##        rep  rank intercept_b0    slope_b1       rsq density   row endrow
##      <num> <int>        <num>       <num>     <num>  <lgcl> <int>  <int>
##   1:    NA     1     450.0090 -0.05928200 0.9913671      NA   234    294
##   2:    NA     2     449.8108 -0.05924967 0.9913695      NA   235    295
##   3:    NA     3     449.5836 -0.05921235 0.9913485      NA   233    293
##   4:    NA     4     449.1063 -0.05913455 0.9914125      NA   236    296
##   5:    NA     5     448.0459 -0.05896063 0.9912072      NA   232    292
##  ---                                                                    
## 237:    NA   237     209.9691 -0.01906573 0.8479699      NA     5     65
## 238:    NA   238     206.9709 -0.01855574 0.8409682      NA     4     64
## 239:    NA   239     204.1786 -0.01808049 0.8364847      NA     3     63
## 240:    NA   240     201.7526 -0.01766716 0.8358341      NA     2     62
## 241:    NA   241     199.4095 -0.01726774 0.8367005      NA     1     61
##         time endtime    oxy endoxy        rate
##        <num>   <num>  <num>  <num>       <num>
##   1: 6078.55 6138.55 89.690 86.082 -0.05928200
##   2: 6079.55 6139.55 89.669 86.034 -0.05924967
##   3: 6077.55 6137.55 89.698 86.105 -0.05921235
##   4: 6080.55 6140.55 89.642 85.986 -0.05913455
##   5: 6076.55 6136.55 89.680 86.153 -0.05896063
##  ---                                          
## 237: 5849.55 5909.55 98.253 97.147 -0.01906573
## 238: 5848.55 5908.55 98.298 97.155 -0.01855574
## 239: 5847.55 5907.55 98.350 97.151 -0.01808049
## 240: 5846.55 5906.55 98.433 97.166 -0.01766716
## 241: 5845.55 5905.55 98.469 97.173 -0.01726774
## 
## Regressions : 241 | Results : 241 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 241 adjusted rate(s):
## Rate          : -0.059282
## Adjustment    : -0.001307744
## Adjusted Rate : -0.05797425 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 16 rate(s) removed, 225 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 224 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0  slope_b1       rsq density row endrow    time endtime
## 1:  NA    1      450.009 -0.059282 0.9913671      NA 234    294 6078.55 6138.55
##      oxy endoxy      rate   adjustment rate.adjusted  rate.input oxy.unit
## 1: 89.69 86.082 -0.059282 -0.001307744   -0.05797425 -0.05797425     %Air
##    time.unit volume      mass area  S  t        P   rate.abs rate.m.spec
## 1:       sec 0.0465 0.0005249   NA 37 27 1.013253 -0.6264924   -1193.546
##    rate.a.spec output.unit rate.output
## 1:          NA  mgO2/hr/kg   -1193.546
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
112	4	CARL355	CARL354	Arlginton reef	221	0.0005249	ch1	Dell	0.0465	2023-05-27	2024-08-16	good/good	37	27	404.6349	0.2123929	0.9904	1193.546	0.6264924	0.9913671	788.9112	0.4140995

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 368 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

5

Enter specimen data

Replicate = 5 
mass = 0.0003949
chamber = "ch4" 
Swim = "good/good"
chamber_vol = chamber4_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "27 May 2023 01 28PM/Oxygen"
experiment_mmr_date2_asus <- "27 May 2023 01 28PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] 0.0006524064

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.001596221

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.32 11.30
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  4  6  7  8  9 11 13 14 15 16 17 18 19 20 21 22 24 25 26
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.32 11.11
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=15, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2_asus$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 10 rate(s) removed, 11 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 5 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0     slope_b1   rsq density  row endrow     time
## 1:   3    1     150.8077 -0.009178138 0.978      NA  801    989  5695.15
## 2:   7    1     170.4369 -0.009161377 0.984      NA 2390   2579  7855.05
## 3:  11    1     196.9114 -0.009810042 0.988      NA 3980   4169 10015.00
## 4:  12    1     189.2486 -0.008574982 0.988      NA 4379   4568 10554.61
## 5:  19    1     220.7990 -0.008531335 0.988      NA 7176   7365 14335.30
## 6:  21    1     227.3376 -0.008340378 0.983      NA 7974   8162 15415.57
##     endtime    oxy endoxy         rate    adjustment rate.adjusted   rate.input
## 1:  5949.96 98.335 96.112 -0.009178138 -1.741557e-05  -0.009160722 -0.009160722
## 2:  8110.68 98.295 96.258 -0.009161377 -6.110527e-04  -0.008550325 -0.008550325
## 3: 10270.52 98.468 96.272 -0.009810042 -1.204576e-03  -0.008605466 -0.008605466
## 4: 10810.15 98.640 96.580 -0.008574982 -1.352859e-03  -0.007222123 -0.007222123
## 5: 14590.97 98.678 96.287 -0.008531335 -2.391783e-03  -0.006139552 -0.006139552
## 6: 15670.32 98.636 96.543 -0.008340378 -2.688507e-03  -0.005651872 -0.005651872
##    oxy.unit time.unit  volume      mass area  S  t        P    rate.abs
## 1:     %Air       sec 0.04791 0.0003949   NA 37 27 1.013253 -0.10199610
## 2:     %Air       sec 0.04791 0.0003949   NA 37 27 1.013253 -0.09519989
## 3:     %Air       sec 0.04791 0.0003949   NA 37 27 1.013253 -0.09581383
## 4:     %Air       sec 0.04791 0.0003949   NA 37 27 1.013253 -0.08041160
## 5:     %Air       sec 0.04791 0.0003949   NA 37 27 1.013253 -0.06835818
## 6:     %Air       sec 0.04791 0.0003949   NA 37 27 1.013253 -0.06292832
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -258.2834          NA  mgO2/hr/kg   -258.2834
## 2:   -241.0734          NA  mgO2/hr/kg   -241.0734
## 3:   -242.6281          NA  mgO2/hr/kg   -242.6281
## 4:   -203.6252          NA  mgO2/hr/kg   -203.6252
## 5:   -173.1025          NA  mgO2/hr/kg   -173.1025
## 6:   -159.3525          NA  mgO2/hr/kg   -159.3525
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
112	5	CARL355	CARL354	Arlginton reef	221	0.0003949	ch4	Asus	0.04791	2023-05-27	2024-08-16	good/good	37	27	223.7425	0.0883559	0.9852

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.51
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  7  8  9 10 11 12 13 14 17 18 19 21 22 23 24 25 26
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.41
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##        rep  rank intercept_b0     slope_b1       rsq density   row endrow
##      <num> <int>        <num>        <num>     <num>  <lgcl> <int>  <int>
##   1:    NA     1     192.3429 -0.033937421 0.9694708      NA    37     82
##   2:    NA     2     192.2687 -0.033909940 0.9693378      NA    38     83
##   3:    NA     3     192.0123 -0.033817071 0.9687541      NA    39     84
##   4:    NA     4     191.8355 -0.033757932 0.9680838      NA    36     81
##   5:    NA     5     191.5036 -0.033634686 0.9678244      NA    40     85
##  ---                                                                     
## 173:    NA   173     108.8710 -0.005335412 0.8446861      NA   169    214
## 174:    NA   174     108.8041 -0.005313194 0.8742757      NA   165    210
## 175:    NA   175     108.4339 -0.005188250 0.8614831      NA   166    211
## 176:    NA   176     108.2582 -0.005128960 0.8568843      NA   167    212
## 177:    NA   177     108.0141 -0.005047055 0.8537538      NA   168    213
##         time endtime    oxy endoxy         rate
##        <num>   <num>  <num>  <num>        <num>
##   1: 2770.10 2830.10 98.211 96.365 -0.033937421
##   2: 2771.45 2831.45 98.265 96.322 -0.033909940
##   3: 2772.82 2832.82 98.214 96.318 -0.033817071
##   4: 2768.72 2828.72 98.225 96.398 -0.033757932
##   5: 2774.16 2834.16 98.213 96.276 -0.033634686
##  ---                                           
## 173: 2949.32 3009.32 93.118 92.708 -0.005335412
## 174: 2943.87 3003.87 93.206 92.870 -0.005313194
## 175: 2945.23 3005.23 93.156 92.901 -0.005188250
## 176: 2946.59 3006.59 93.148 92.867 -0.005128960
## 177: 2947.96 3007.96 93.167 92.845 -0.005047055
## 
## Regressions : 177 | Results : 177 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 177 adjusted rate(s):
## Rate          : -0.03393742
## Adjustment    : 0.0006524064
## Adjusted Rate : -0.03458983 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 43 rate(s) removed, 134 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 133 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow   time
## 1:  NA    1     192.3429 -0.03393742 0.9694708      NA  37     82 2770.1
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1:  2830.1 98.211 96.365 -0.03393742 0.0006524064   -0.03458983 -0.03458983
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04791 0.0003949   NA 37 27 1.013253 -0.3851255
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -975.2481          NA  mgO2/hr/kg   -975.2481
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
112	5	CARL355	CARL354	Arlginton reef	221	0.0003949	ch4	Asus	0.04791	2023-05-27	2024-08-16	good/good	37	27	223.7425	0.0883559	0.9852	975.2481	0.3851255	0.9694708	751.5055	0.2967695
### Expor	ting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 369 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

6

Enter specimen data

Replicate = 6 
mass = 0.0004313
chamber = "ch3" 
Swim = "good/good"
chamber_vol = chamber3_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "27 May 2023 01 39PM/Oxygen"
experiment_mmr_date2_asus <- "27 May 2023 01 39PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] -0.001731714

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.004176906

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.32 11.30
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  4  6  7  8  9 11 13 14 15 16 17 18 19 20 21 22 24 25 26
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.32 11.11
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=15, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 1 rate(s) removed, 20 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 14 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   3    1     172.9036 -0.01334978 0.980      NA  801    982  5695.15
## 2:   4    1     181.8273 -0.01342150 0.966      NA 1199   1380  6234.35
## 3:  14    1     268.1093 -0.01459486 0.978      NA 5178   5360 11635.35
## 4:  16    1     290.6131 -0.01512762 0.994      NA 5977   6158 12714.48
## 5:  18    1     244.5681 -0.01063771 0.991      NA 6776   6957 13794.70
## 6:  20    1     278.0448 -0.01210197 0.992      NA 7575   7756 14875.21
##     endtime    oxy endoxy        rate   adjustment rate.adjusted   rate.input
## 1:  5940.48 97.045 93.545 -0.01334978 -0.002458672  -0.010891104 -0.010891104
## 2:  6479.37 97.766 94.519 -0.01342150 -0.002619746  -0.010801751 -0.010801751
## 3: 11881.07 97.989 94.591 -0.01459486 -0.004233743  -0.010361119 -0.010361119
## 4: 12959.06 98.157 94.578 -0.01512762 -0.004556032  -0.010571588 -0.010571588
## 5: 14039.27 97.947 95.248 -0.01063771 -0.004878815  -0.005758890 -0.005758890
## 6: 15120.51 97.923 94.876 -0.01210197 -0.005201795  -0.006900175 -0.006900175
##    oxy.unit time.unit  volume      mass area  S  t        P    rate.abs
## 1:     %Air       sec 0.04551 0.0004313   NA 37 27 1.013253 -0.11518778
## 2:     %Air       sec 0.04551 0.0004313   NA 37 27 1.013253 -0.11424275
## 3:     %Air       sec 0.04551 0.0004313   NA 37 27 1.013253 -0.10958249
## 4:     %Air       sec 0.04551 0.0004313   NA 37 27 1.013253 -0.11180848
## 5:     %Air       sec 0.04551 0.0004313   NA 37 27 1.013253 -0.06090786
## 6:     %Air       sec 0.04551 0.0004313   NA 37 27 1.013253 -0.07297845
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -267.0711          NA  mgO2/hr/kg   -267.0711
## 2:   -264.8800          NA  mgO2/hr/kg   -264.8800
## 3:   -254.0749          NA  mgO2/hr/kg   -254.0749
## 4:   -259.2360          NA  mgO2/hr/kg   -259.2360
## 5:   -141.2192          NA  mgO2/hr/kg   -141.2192
## 6:   -169.2058          NA  mgO2/hr/kg   -169.2058
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
112	6	CARL355	CARL354	Arlginton reef	221	0.0004313	ch3	Asus	0.04551	2023-05-27	2024-08-16	good/good	37	27	242.8936	0.10476	0.982

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.51
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row,
              to = cycle1.end.row,
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  4  5  6  7  8  9 10 11 12 13 15 16 17 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.44
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##        rep  rank intercept_b0     slope_b1       rsq density   row endrow
##      <num> <int>        <num>        <num>     <num>  <lgcl> <int>  <int>
##   1:    NA     1     261.6840 -0.047974511 0.9978243      NA    15     60
##   2:    NA     2     261.5192 -0.047925410 0.9976948      NA    16     61
##   3:    NA     3     261.2899 -0.047857792 0.9975471      NA    17     62
##   4:    NA     4     260.7054 -0.047691306 0.9968527      NA    14     59
##   5:    NA     5     260.5495 -0.047642344 0.9973865      NA    18     63
##  ---                                                                     
## 172:    NA   172     125.2622 -0.009294574 0.8363176      NA   139    184
## 173:    NA   173     124.4617 -0.009073421 0.8374271      NA   140    185
## 174:    NA   174     123.4420 -0.008791991 0.8449233      NA   141    186
## 175:    NA   175     123.1627 -0.008717943 0.8428205      NA   143    188
## 176:    NA   176     122.7551 -0.008602980 0.8516608      NA   142    187
##         time endtime    oxy endoxy         rate
##        <num>   <num>  <num>  <num>        <num>
##   1: 3407.80 3467.80 98.065 95.371 -0.047974511
##   2: 3409.15 3469.15 98.111 95.358 -0.047925410
##   3: 3410.50 3470.50 98.070 95.286 -0.047857792
##   4: 3406.42 3466.42 98.117 95.370 -0.047691306
##   5: 3411.84 3471.84 98.089 95.211 -0.047642344
##  ---                                           
## 172: 3575.94 3635.94 92.124 91.466 -0.009294574
## 173: 3577.30 3637.30 92.132 91.444 -0.009073421
## 174: 3578.65 3638.65 92.156 91.416 -0.008791991
## 175: 3581.32 3641.32 92.094 91.228 -0.008717943
## 176: 3579.98 3639.98 92.119 91.378 -0.008602980
## 
## Regressions : 176 | Results : 176 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 176 adjusted rate(s):
## Rate          : -0.04797451
## Adjustment    : -0.001731714
## Adjusted Rate : -0.0462428 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 76 rate(s) removed, 100 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 99 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow   time
## 1:  NA    1      261.684 -0.04797451 0.9978243      NA  15     60 3407.8
##    endtime    oxy endoxy        rate   adjustment rate.adjusted rate.input
## 1:  3467.8 98.065 95.371 -0.04797451 -0.001731714    -0.0462428 -0.0462428
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04551 0.0004313   NA 37 27 1.013253 -0.4890785
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1133.964          NA  mgO2/hr/kg   -1133.964
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
112	6	CARL355	CARL354	Arlginton reef	221	0.0004313	ch3	Asus	0.04551	2023-05-27	2024-08-16	good/good	37	27	242.8936	0.10476	0.982	1133.964	0.4890785	0.9978243	891.0701	0.3843185
### Expor	ting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 370 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

7

Enter specimen data

Replicate = 7 
mass = 0.0004343 
chamber = "ch2" 
Swim = "good/good"
chamber_vol = chamber2_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "27 May 2023 01 48PM/Oxygen"
experiment_mmr_date2_asus <- "27 May 2023 01 48PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] 0.0001511001

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean) #,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.001881849

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.32 11.30
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  4  6  7  8  9 11 13 14 15 16 17 18 19 20 21 22 24 25 26
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.32 11.11
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=15, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 1 rate(s) removed, 20 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 14 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   6    1     231.3284 -0.01819812 0.994      NA 1997   2178  7314.67
## 2:   7    1     241.8490 -0.01825988 0.997      NA 2390   2571  7855.05
## 3:  15    1     297.8879 -0.01638224 0.994      NA 5578   5759 12175.37
## 4:  16    1     367.0649 -0.02108607 0.975      NA 5977   6158 12714.48
## 5:  20    1     413.3225 -0.02110658 0.979      NA 7575   7756 14875.21
## 6:  21    1     432.4929 -0.02165230 0.950      NA 7974   8155 15415.57
##     endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1:  7559.57 98.262 93.687 -0.01819812 -0.0008555908   -0.01734253 -0.01734253
## 2:  8099.87 98.377 93.842 -0.01825988 -0.0009898305   -0.01727005 -0.01727005
## 3: 12419.85 98.369 94.445 -0.01638224 -0.0020631097   -0.01431913 -0.01431913
## 4: 12959.06 98.794 93.617 -0.02108607 -0.0021970562   -0.01888902 -0.01888902
## 5: 15120.51 98.881 93.656 -0.02110658 -0.0027339480   -0.01837263 -0.01837263
## 6: 15660.82 99.031 93.956 -0.02165230 -0.0028681864   -0.01878411 -0.01878411
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04573 0.0004343   NA 37 27 1.013253 -0.1843068
## 2:     %Air       sec 0.04573 0.0004343   NA 37 27 1.013253 -0.1835365
## 3:     %Air       sec 0.04573 0.0004343   NA 37 27 1.013253 -0.1521757
## 4:     %Air       sec 0.04573 0.0004343   NA 37 27 1.013253 -0.2007420
## 5:     %Air       sec 0.04573 0.0004343   NA 37 27 1.013253 -0.1952541
## 6:     %Air       sec 0.04573 0.0004343   NA 37 27 1.013253 -0.1996271
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -424.3766          NA  mgO2/hr/kg   -424.3766
## 2:   -422.6030          NA  mgO2/hr/kg   -422.6030
## 3:   -350.3931          NA  mgO2/hr/kg   -350.3931
## 4:   -462.2196          NA  mgO2/hr/kg   -462.2196
## 5:   -449.5835          NA  mgO2/hr/kg   -449.5835
## 6:   -459.6526          NA  mgO2/hr/kg   -459.6526
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
112	7	CARL355	CARL354	Arlginton reef	221	0.0004343	ch2	Asus	0.04573	2023-05-27	2024-08-16	good/good	37	27	443.6871	0.1926933	0.979

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.33 11.30
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row,
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  3  4  5  9 10 12 13 14 15 16 17 18 19 20 22 24 25 26 28
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.44
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##        rep  rank intercept_b0    slope_b1       rsq density   row endrow
##      <num> <int>        <num>       <num>     <num>  <lgcl> <int>  <int>
##   1:    NA     1     356.3082 -0.06567373 0.9970038      NA    47     92
##   2:    NA     2     356.3041 -0.06567361 0.9970041      NA    46     91
##   3:    NA     3     356.1021 -0.06562385 0.9969450      NA    45     90
##   4:    NA     4     355.7918 -0.06554366 0.9968404      NA    48     93
##   5:    NA     5     355.7657 -0.06554048 0.9968370      NA    44     89
##  ---                                                                    
## 173:    NA   173     259.2898 -0.04124815 0.9481676      NA    13     58
## 174:    NA   174     259.2567 -0.04124799 0.9481315      NA    16     61
## 175:    NA   175     258.1117 -0.04124453 0.9922144      NA    83    128
## 176:    NA   176     258.9907 -0.04117768 0.9486127      NA    15     60
## 177:    NA   177     258.9779 -0.04117185 0.9486348      NA    14     59
##         time endtime    oxy endoxy        rate
##        <num>   <num>  <num>  <num>       <num>
##   1: 3965.78 4025.78 95.840 92.001 -0.06567373
##   2: 3964.42 4024.42 95.923 92.067 -0.06567361
##   3: 3963.06 4023.06 95.993 92.154 -0.06562385
##   4: 3967.14 4027.14 95.788 91.941 -0.06554366
##   5: 3961.73 4021.73 96.055 92.255 -0.06554048
##  ---                                          
## 173: 3919.73 3979.73 97.877 94.976 -0.04124815
## 174: 3923.78 3983.78 97.708 94.656 -0.04124799
## 175: 4014.63 4074.63 92.741 89.902 -0.04124453
## 176: 3922.43 3982.43 97.750 94.777 -0.04117768
## 177: 3921.08 3981.08 97.841 94.855 -0.04117185
## 
## Regressions : 177 | Results : 177 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 177 adjusted rate(s):
## Rate          : -0.06567373
## Adjustment    : 0.0001511001
## Adjusted Rate : -0.06582483 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 0 rate(s) removed, 177 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 176 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     356.3082 -0.06567373 0.9970038      NA  47     92 3965.78
##    endtime   oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 4025.78 95.84 92.001 -0.06567373 0.0001511001   -0.06582483 -0.06582483
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04573 0.0004343   NA 37 27 1.013253 -0.6995497
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1610.752          NA  mgO2/hr/kg   -1610.752
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
112	7	CARL355	CARL354	Arlginton reef	221	0.0004343	ch2	Asus	0.04573	2023-05-27	2024-08-16	good/good	37	27	443.6871	0.1926933	0.979	1610.752	0.6995497	0.9970038	1167.065	0.5068564
### Expor	ting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 371 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

8

Enter specimen data

Replicate = 8 
mass = 0.0005437 
chamber = "ch1" 
Swim = "good/good"
chamber_vol = chamber1_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "27 May 2023 01 59PM/Oxygen"
experiment_mmr_date2_asus <- "27 May 2023 01 59PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] -0.001619218

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.002815871

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.32 11.30
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  4  6  7  8  9 11 13 14 15 16 17 18 19 20 21 22 24 25 26
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.32 11.11
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=15, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 4 rate(s) removed, 17 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 11 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0     slope_b1   rsq density  row endrow     time
## 1:   5    1     146.5313 -0.007232215 0.958      NA 1598   1779  6774.74
## 2:   6    1     156.1505 -0.008002557 0.952      NA 1997   2178  7314.67
## 3:  14    1     199.3012 -0.008696421 0.958      NA 5178   5360 11635.35
## 4:  15    1     199.6862 -0.008360129 0.984      NA 5578   5759 12175.37
## 5:  16    1     212.4085 -0.008990812 0.965      NA 5977   6158 12714.48
## 6:  21    1     234.8891 -0.008866294 0.972      NA 7974   8155 15415.57
##     endtime    oxy endoxy         rate   adjustment rate.adjusted   rate.input
## 1:  7019.94 97.419 95.541 -0.007232215 -0.002132850  -0.005099365 -0.005099365
## 2:  7559.57 97.589 95.338 -0.008002557 -0.002211786  -0.005790772 -0.005790772
## 3: 11881.07 97.806 95.832 -0.008696421 -0.002843687  -0.005852735 -0.005852735
## 4: 12419.85 97.848 95.687 -0.008360129 -0.002922567  -0.005437563 -0.005437563
## 5: 12959.06 97.849 95.597 -0.008990812 -0.003001412  -0.005989401 -0.005989401
## 6: 15660.82 97.839 95.902 -0.008866294 -0.003396459  -0.005469836 -0.005469836
##    oxy.unit time.unit  volume      mass area  S  t        P    rate.abs
## 1:     %Air       sec 0.04565 0.0005437   NA 37 27 1.013253 -0.05409842
## 2:     %Air       sec 0.04565 0.0005437   NA 37 27 1.013253 -0.06143345
## 3:     %Air       sec 0.04565 0.0005437   NA 37 27 1.013253 -0.06209080
## 4:     %Air       sec 0.04565 0.0005437   NA 37 27 1.013253 -0.05768631
## 5:     %Air       sec 0.04565 0.0005437   NA 37 27 1.013253 -0.06354067
## 6:     %Air       sec 0.04565 0.0005437   NA 37 27 1.013253 -0.05802869
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -99.50049          NA  mgO2/hr/kg   -99.50049
## 2:  -112.99144          NA  mgO2/hr/kg  -112.99144
## 3:  -114.20049          NA  mgO2/hr/kg  -114.20049
## 4:  -106.09951          NA  mgO2/hr/kg  -106.09951
## 5:  -116.86716          NA  mgO2/hr/kg  -116.86716
## 6:  -106.72924          NA  mgO2/hr/kg  -106.72924
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
112	8	CARL355	CARL354	Arlginton reef	221	0.0005437	ch1	Asus	0.04565	2023-05-27	2024-08-16	good/good	37	27	111.3776	0.060556	0.9662

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.32 11.30
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row,  
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  3  4  5  6  7  8  9 10 12 13 14 15 16 18 19 20 21 23 24
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.48
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##        rep  rank intercept_b0    slope_b1       rsq density   row endrow
##      <num> <int>        <num>       <num>     <num>  <lgcl> <int>  <int>
##   1:    NA     1     238.4760 -0.02985346 0.9896477      NA   176    221
##   2:    NA     2     236.9512 -0.02954038 0.9891410      NA   175    220
##   3:    NA     3     235.8727 -0.02931905 0.9884741      NA   174    219
##   4:    NA     4     234.0211 -0.02893855 0.9879940      NA   173    218
##   5:    NA     5     231.2222 -0.02836335 0.9865606      NA   172    217
##  ---                                                                    
## 172:    NA   172     153.9895 -0.01215057 0.8786253      NA    51     96
## 173:    NA   173     153.7662 -0.01210247 0.8787859      NA    50     95
## 174:    NA   174     153.0750 -0.01193829 0.9379618      NA     5     50
## 175:    NA   175     152.9778 -0.01191781 0.9373600      NA     4     49
## 176:    NA   176     152.9667 -0.01191636 0.9372954      NA     3     48
##         time endtime    oxy endoxy        rate
##        <num>   <num>  <num>  <num>       <num>
##   1: 4838.94 4898.94 93.966 92.171 -0.02985346
##   2: 4837.60 4897.60 94.000 92.257 -0.02954038
##   3: 4836.21 4896.21 94.018 92.245 -0.02931905
##   4: 4834.86 4894.86 93.983 92.268 -0.02893855
##   5: 4833.48 4893.48 94.081 92.303 -0.02836335
##  ---                                          
## 172: 4668.77 4728.77 97.324 96.462 -0.01215057
## 173: 4667.42 4727.42 97.410 96.509 -0.01210247
## 174: 4606.46 4666.46 98.037 97.410 -0.01193829
## 175: 4605.13 4665.13 98.005 97.476 -0.01191781
## 176: 4603.78 4663.78 98.181 97.474 -0.01191636
## 
## Regressions : 176 | Results : 176 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 176 adjusted rate(s):
## Rate          : -0.02985346
## Adjustment    : -0.001619218
## Adjusted Rate : -0.02823424 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 43 rate(s) removed, 133 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 132 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1      238.476 -0.02985346 0.9896477      NA 176    221 4838.94
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 4898.94 93.966 92.171 -0.02985346 -0.001619218   -0.02823424 -0.02823424
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04565 0.0005437   NA 37 27 1.013253 -0.2995329
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -550.9158          NA  mgO2/hr/kg   -550.9158
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
112	8	CARL355	CARL354	Arlginton reef	221	0.0005437	ch1	Asus	0.04565	2023-05-27	2024-08-16	good/good	37	27	111.3776	0.060556	0.9662	550.9158	0.2995329	0.9896477	439.5383	0.238977
### Expor	ting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 372 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)